sign in sign up
<<
Home , Semantic integration, Semantic matching, Semantic web, Semantic web service

Journal of Computing, Volume 2, Issue 6, June 2010, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING/ WWW.JOURNALOFCOMPUTING.ORG 182 Understanding and Ontologies: Theory and Applications

Mohammad Mustafa Taye Department of Software Engineering Faculty of Technology Philadelphia University, Amman, Jordan

Abstract — Semantic Web is actually an extension of the current one in that it represents information more meaningfully for humans and computers alike. It enables the description of contents and services in machine-readable form, and enables annotating, discovering, publishing, advertising and composing services to be automated. It was developed based on Ontology, which is considered as the backbone of the Semantic Web. In other words, the current Web is transformed from being machine-readable to machine-understandable. In fact, Ontology is a key technique with which to annotate and provide a common, comprehensible foundation for resources on the Semantic Web. Moreover, Ontology can provide a common vocabulary, a grammar for publishing data, and can supply a semantic description of data which can be used to preserve the Ontologies and keep them ready for . This paper provides basic of web services and the Semantic Web, defines the structure and the main applications of ontology, and provides many relevant terms are explained in order to provide a basic understanding of ontologies.

Index Terms — Ontology, Semantic Web, Web Services.

and computers alike. It enables the 1 INTRODUCTION description of contents and services in One of the most interesting inventions, in machine-readable form, and enables recent decades, is that of Web Services [36]. annotating, discovering, publishing, These are computer program advertising and composing services to be “applications”: self-describing, self- automated. It was developed based on contained applications whose function is to Ontology, which is considered as the automatically share information over the backbone of the Semantic Web. In other with other applications. Some words, the current Web is transformed weaknesses such as browsing information from being machine-readable to machine- without taking its meaning into account understandable. have recently appeared in Web Services. One function of the Web is to build a source This creates a need for a new Web with of reference for information on several more relevance to the user. subjects, while the Semantic Web is Semantic Web is actually an extension of designed to build a web of meaning. The the current one in that it represents foundation of vocabularies and effective information more meaningfully for humans communication on the Semantic Web is

183 ontology. “Ontology provides a formal, be described by the three actions of publish, explicit specification of a shared bind and find, and three actors: the service conceptualisation of a domain” [31, 36]. requester, the service provider and the Therefore, it facilitates knowledge sharing registry, where services can be published, over distributed systems; in other words, it advertised and sometimes located. In other allows systems or applications to cooperate words, service providers describe and that were not formerly designed to advertise their services in the registry, interoperate. Ontology plays a major part in while service requesters search the registry solving the problem of interoperability for services that match their requirements. between applications across different There are obviously many examples of Web organizations, by providing a shared services, including: understanding of common domains.  Credit card authorisation. Several Ontologies have recently been built.  Currency converter (e.g. dollars to Consequently, they should be accessed Euros). from other applications for use or  Stock quote provider. information exchange. Ontologies in such  Shipping rate calculator. numbers present interoperability problems, for which many solutions have been 3 SEMANTIC WEB developed. One of these is to build a single Ontology, but this is inadequate, partly The Semantic Web [36] is distributed and because it is too inflexible for knowledge heterogeneous, has brought the evolution sharing. Another solution is Ontology of the Web to a higher level. There are two Mapping which plays an important role in visions of the future in the development of solving interoperability in heterogeneous the Web, the first being to improve its systems and in many application domains. usability as a medium for collaboration and This way is to build bridges between the second to ensure that its contents can be Ontologies in order to provide commonly understood by machines. Providing accessible layers that could then exchange annotation data will facilitate this second information in semantically sound ways. aim. Therefore, in the following sections, Tim Berners-Lee, who invented the WWW this paper will describe many terminologies and has worked on the Semantic Web, in order to understand the Semantic Web states that the latter “is not a separate Web and Ontologies. but an extension of the current one, in which information is given a well-defined meaning, better enabling computers and 2. WEB SERVICE people to work in cooperation.” [2]. Thus, A web service [36] is a self-describing the Semantic Web [16, 31] is distinguished software program using self-contained by a more meaningful representation of applications and identified by a Uniform information for humans and computers, Resource Identifier (URI), used to share providing a description of its contents and information between applications over the services in machine-readable form; Internet. Access to and retrieval of moreover, it enables services to be information from the Web occurs via the automatically annotated, discovered, HTTP protocol. One of the first published, advertised and composed. It to have been used for the internet is HTML, thereby facilitates interoperability and the a markup used to describe the sharing of knowledge over the Web. Its document structure. The Web can be main goal is therefore to make information conceived as a huge library containing a on the Web accessible and understandable large amount of information or data – by humans and computers. Figure 1 unfortunately without any sensible means illustrates the architecture of the Semantic of representation. Web. The common Web service scenario [36] can

184

In fact, both the Semantic Web and Web the basis of a rapidly growing number services are considered to be a set of of software development activities. resources, identified by the URI. The Each document starts with a difference between them is that Web namespace declaration using XML services use HTTP to display the contents Namespace. of a page, while the Semantic Web tries to  The Resource Description Framework create machine readability by semantically (RDF) is the first layer of the Semantic representing the data or information in Web. RDF is a framework for using resources. Numerous tools and applications and representing and of Semantic Web technologies have recently describing the semantics of become available. information about resources on the Web in a machine-accessible way. It uses URIs to identify Web resources and to describe the relations between these resources, using a graph model. While describing classes of resources and the properties between them, using RDF Schema (which is a simple modelling language), it also provides a simple reasoning framework for inferring types of resources.  Ontology Vocabulary is a language which provides a common vocabulary and grammar for published data as well as a semantic description of the data used to preserve the ontologies Figure 1: Semantic Web architecture [2] and to keep them ready for inference. Ontology means describing the The layers of architecture represented [2, semantics of the data, providing a 16, 31, 36, 35] in Figure 1 are briefly uniform way to enable communication described below: by which different parties can  URI and : To identify and understand each other.  locate resources, or indeed anything Logic and Proof: In the Semantic Web, on the Web, a uniform system of the building of systems follows a logic identifiers (URIs) is used. The URI, which considers the structure of which is considered to be the ontology. A reasoner could be used to foundation of the Web, is used to give check and resolve consistency a unique name to each resource. problems and the redundancy of the Unicode is the standard for computer translation. A reasoning character representation. system is used to make new .   Extensible Markup Language (XML) Trust is the final layer of the Semantic is a markup language, which means Web. This component concerns the that it is machine-readable and has its trustworthiness of the information on own format. It is widely known in the the Web in order to provide an WWW community because it has a assurance of its quality. flexible text format and was designed to describe data and to meet the 4 ONTOLOGY challenges of large-scale e-business Ontologies [27], which are used in order to and electronic publishing; it plays an support interoperability and common important role in exchanging different understanding between the different types of data on the Web. In fact, it is

185 parties, are a key component in solving the disciplines, such as software engineering problem of , thus and AI, it is defined as “a formal explicit enabling semantic interoperability between specification of a shared conceptualization” different web applications and services. [12]. The foundations of this definition are: Recently, ontologies have become a popular  All knowledge (e.g. the type of research topic in many communities, concepts used and the constraints on including , their use) in ontology must have an electronic commerce, knowledge explicit specification. management and natural language  An ontology is a conceptualisation, processing. Ontologies provide a common which means it has a universally understanding of a domain that can be comprehensible concept. communicated between people, and of  “Shared” indicates an agreement heterogeneous and widely spread about the meaning in such domains. In application systems. In fact, they have been other words, an ontology should developed in (AI) capture consensual knowledge research communities to facilitate accepted by the communities. knowledge sharing and reuse.  “Formal” refers to the grounding of The goal of an ontology is to achieve a representation in well understood common and shared knowledge that can be logic, and any ontology should be transmitted between people and between machine-processable. application systems. Thus, ontologies [16] play an important role in achieving 4.1 ONTOLOGY REPRESENTATION interoperability across organizations and on the Semantic Web [36], because they aim Ontology is comprised of four main to capture domain knowledge and their components: concepts, instances, relations role is to create semantics explicitly in a and axioms. The present research adopts generic way, providing the basis for the following definitions of these agreement within a domain. Ontology is ontological components: used to enable interoperation between Web  A Concept (also known as a class or a applications from different areas or from term) is an abstract group, set or different views on one area. For that reason, collection of objects. It is the it is necessary to establish mappings among fundamental element of the domain concepts of different ontologies to capture and usually represents a group or class the semantic correspondence between whose members share common them. However, establishing such a properties. This component is correspondence is not an easy task [31]. represented in hierarchical graphs, Because there are many different such that it looks similar to object- definitions of ontology, it is very difficult to oriented systems. The concept is find a definition that researchers can agree represented by a “super-class”, upon. The present research first presents representing the higher class or so- some of these definitions which have been called “parent class”, and a “subclass” given from different perspectives, and then which represents the subordinate or explores in depth those aspects of these so-called “child class”. For instance, a definitions which are related to the topic university could be represented as a under investigation. class with many subclasses, such as The primary use of the word “ontology” is faculties, libraries and employees. in the discipline of philosophy, where it  An Instance (also known as an means “the study or theory of the individual) is the “ground-level” explanation of being”; it thus defines an component of an ontology which entity or being and its relationship with and represents a specific object or element activity in its environment. In other of a concept or class. For example,

186

“Jordan” could be an instance of the  HR represents a class “Arab countries” or simply relation hierarchy in the form of a “countries”. relation HR  R×R, where HR (R1, R2)  A Relation (also known as a slot) is denotes that R1 is a subrelation of used to express relationships between R2(“rdfs:subPropertyOf”). two concepts in a given domain. More specifically, it describes the  I is the instantiation of relationship between the first concept, the concepts in a particular domain represented in the domain, and the (“rdf:type”). second, represented in the range. For example, “study” could be represented In general, there are many ways to as a relationship between the concept represent or model the classification of “person” (which is a concept in the concepts semantically. These include domain) and “university” or “college” , thesauri and ontologies. These (which is a concept in the range). widely varying concepts are used in Web  An Axiom is used to impose semantics, which is why it is necessary to constraints on the values of classes or apply , or the science of instances, so axioms are generally identifying and arranging vocabulary in the expressed using logic-based languages shape of a hierarchy or tree. In other words, such as first-order logic; they are used it is used to describe concepts and their to verify the consistency of the relationships explicitly. The relationships of ontology. “subclass” and “super-class” are the taxonomic ones that could be used. 4.2 STRUCTURE OF ONTOLOGY A thesaurus [26] is a vocabulary with conceptual relationships between the terms In general, the structure of an and can be considered an extension of ontology [28] is described as a taxonomy with richer semantic relationships. It can easily be converted into C R 5‐tuple O: = (C, H , R, H , I), a taxonomy, but expressiveness and semantics will be lost. The relationships where which could be used in a thesaurus are equivalence, homography, hierarchy and  C represents a set of association. concepts (instances of “rdf:Class”). Ontologies are like taxonomies but with These concepts are arranged with a more semantic relationships between corresponding subsumption hierarchy concepts and attributes; they also contain C H . strict rules used to represent concepts and relationships. An ontology is a  R represents a set of hierarchically structured set of terms for relations that relate concepts to one describing a domain that can be used as a another (instances of “rdf:Property”). Ri skeletal foundation for a .  R and Ri  C × C. According to this definition, the same ontology can be used for building several  HC represents a knowledge bases, which would share the concept hierarchy in the form of a same skeleton or taxonomy. relation (a binary relation The ontology community distinguishes corresponding to “rdfs:subClassOf”). HC ontologies that are mainly taxonomies from  C × C, where HC (C1, C2) denotes that those that model the domain in a deeper C1 is a subconcept of C2. way and provide more restrictions on domain semantics. The community calls them lightweight and heavyweight

187 ontologies respectively. The former include [2]. concepts, concept taxonomies, relationships between concepts, and properties that 4.3 ONTOLOGY APPLICATIONS describe concepts, while heavyweight Over the years, ontology has become a ontologies add axioms and constraints to popular research topic in a range of lightweight ones. These clarify the intended disciplines, with the aim of increasing meaning of the terms gathered in the understanding of and building a consensus ontology. in a given area of knowledge. Ontology also Heavyweight and lightweight ontologies leads to the sharing of knowledge between can be modelled with different knowledge systems and people. As mentioned modelling techniques and can be previously, ontology first appeared in AI implemented in various kinds of languages laboratories, before being used in other [21]. Ontologies can be: fields; for example:  highly informal if they are expressed in  Semantic Web [2]: Ontology plays a key natural language; According to this, a role in the Semantic Web in supporting highly informal ontology would not be information exchange across distributed an ontology, since it is not machine- environments. The Semantic Web readable. represents data in a machine-  semi-informal if expressed in a processable way, which is why it is restricted and structured form of natural considered to be an extension of the language, since it is a machine-readable; current Web.  semi-formal if expressed in an artificial  Discovery [2]: In and formally defined language (e.g. RDF the e-business environment, ontology graphs); and plays an important role by finding the  rigorously formal if they provide best match for the requester looking for meticulously defined terms with formal merchandise or something else. It also semantics, theorems and proof of helps online travel customers obtain a properties such as soundness and response. completeness (e.g. Web Ontology  Artificial Intelligence [27]: Ontology Language [OWL]). has been developed in the AI research The expressiveness of an ontology is based community, its goal here being to on the degree of explication of the (meta-) facilitate the sharing of knowledge and knowledge. Several ontologies capture the reuse and enabling of processing specific domains or certain applications, between programs, services, agents or while others try to capture all terms in organisations across a given domain. natural language. Ontologies that capture  Multi-agent [16]: The importance of extra relations and extra constraints are ontology in this area is that it provides a considered to be more expressive, because shared understanding of domain they capture knowledge of the domain on a knowledge, allowing for easy more detailed level. On the other hand, the communication between agents and expressiveness of an ontology is restricted thereby reducing misunderstandings. by the languages used for describing or  Search Engines [16, 31]: These use specifying it. Ontology languages can be ontology in the form of thesauri to find seen as restricting the expressiveness of the the of search terms, which ontology [38]. facilitates internet searching. An ontology is expressed in a knowledge  E-Commerce [16]: This application uses representation language, which provides a ontology to facilitate communication formal frame of semantics. This ensures between seller and buyer through the that the specification of domain knowledge description of merchandise, as well as in an ontology is machine-processable and enabling machine-based is being interpreted in a well-defined way

188

communication.  Interoperability [7]: The problem of bringing together heterogeneous and distributed systems is known as the “interoperability problem”. In this area, the importance of applying ontology appears explicitly: it is used to integrate different heterogeneous application systems. In the field of services, ontology plays the major role of providing a richer description of these services and terms and the relationships between them in the application domain, leading to a capture of Figure 2: Ontology in service over the internet the domain of knowledge in an explicitly representative manner. At the same time, it 4.4 ONTOLOGY INTEROPERABILITY supports the inference of implied knowledge by declaring the descriptions. This section describes several operations The following example is given in order to on ontologies. demonstrate the reasons for considering ontology to be the backbone of the Semantic Web. As mentioned in [25], it 4.4.1 ONTOLOGY TRANSFORMATION AND illustrates how ontology may be used to TRANSLATION match services with semantic meanings. Ontology Transformation [6, 9] is the According to this scenario, the service process used to develop a new ontology to requester invokes a service (for example, cope with new requirements made by an buying a car), which triggers a description existing one for a new purpose, by using a of the service request information transformation function t. In this operation, annotated in metadata. Service providers many changes are possible, including also describe and advertise their services in changes in the semantics of the ontology metadata to provide answers to the and changes in the representation requester, while the service match engine formalism. receives the metadata of both provider and Ontology Translation is the function of requester, upon which it accesses the translating the representation formalism of ontology, which provides a possible an ontology while keeping the same identification of “automobile” and semantic. In other words, it is the process of “vehicle” with “car”. The service match change or modification of the structure of engine will infer from this whether the an ontology in order to make it suitable for request has been satisfied or not (see Figure purposes other than the original one. 2). There are two types of translation. The first is translation from one formal language to another, for example from RDFS to OWL, called syntactic translation. The second is translation of vocabularies, called [6]. The translation problem arises when two Web-based agents attempt to exchange information, describing it using different ontologies.

189

4.4.2 ONTOLOGY MERGING ontologies by bringing them into mutual Ontology merging [20, 23, 30] is the process agreement. This method is near to artificial of creating a new single coherent ontology intelligence methods: being a logical from two or more existing source relation, ontology alignments are used to ontologies related to the same domain. The clearly describe how the concepts in the new ontology will replace the source different ontologies are logically related. ontologies. This means that additional axioms describe the relationship between the concepts in different ontologies without changing the 4.4.3 ONTOLOGY INTEGRATION meaning in the original ontologies. Integration [23, 30] is the process of creating In fact, uses as pre- a new ontology from two or more source process for both ontology merging and ontologies from different domains. ontology integration. There are many different definitions of 4.4.4 ONTOLOGY MAPPING ontology alignment, depending upon its Ontology mapping [19, 22, 29, 32, 34] is a application and its intended outcome. formal expression or process that defines Sample definitions include the following: the semantic relationships between entities  Ontology alignment is used to from different ontologies. In other words, it “establish correspondences among the is an important operator in many ontology source ontologies, and to determine application domains, such as the Semantic the set of overlapping concepts, Web and e-commerce, which are used to concepts that are similar in meaning describe how to connect and from but have different names or structure, correspondences between entities across and concepts that are unique to each of different ontologies. Ontology matching is the sources” [30]. the process of discovering similarities  Ontology alignment is the process of between two ontologies. bringing two or more ontologies into An entity e is understood in an ontology O mutual agreement, making them denoted by e|O is concept C, relation R, or consistent and coherent [7, 10, 33]. instance I, i.e. e|O  C  R  I. Mapping  “Given two ontologies O1 and O2, the two ontologies, O1 onto O2, means that mapping one ontology onto another each entity in ontology O1 is trying to find means that each entity (concept C, a corresponding entity which has the same relation R, or instance I) in ontology intended meaning in ontology O2. O1 is trying to find a corresponding The Ontology mapping function “map” is entity (i.e. by using matching defined based on the vocabulary, E, of all algorithms), which has the same terms e  E and based on the set of possible intended meaning, in ontology O2” ontologies, O as a partial function: [11]. Formally, an ontology alignment map: E × O × O ֊ E, with function is defined as follows: e  O1(  f O2 : map(e,O1,O2) = f  An ontology alignment function, align, map(e,O1,O2) = ). based on the set E of all entities e  E and based on the set of possible ontologies O, An entity is mapped to another entity or is a partial function. none. Align: O1  O2 4.4.5 ONTOLOGY ALIGNMENT Ontology alignment [12, 13, 14, 24] is the Align (eO1) = fO2 if Sim(eO1, fO2) > process or method of creating a consistent threshold. and coherent link between two or more

190

Where Oi: ontology, eOi, fOj: entities of Indeed, some of these languages have the (Oi, Oj) Sim (eO1, fO2): similarity function ability to represent certain logical relations between two entities eO1 and fO2. which others do not. Because some languages have greater expressive power The ontology alignment function is based than others, their selection for representing on different similarity measures. ontologies is based mainly on what the ontology represents or what it will be used A similarity measure is a real‐valued for. In other words, different kinds of function Sim (ei, fj): O×O  [0, 1] ontological knowledge-based applications measuring the degree of similarity need different language facilitators to between x and y. enable reasoning on ontology data. These description languages provide richer constructors for forming complex class expressions and axioms. In fact, most recent ontology developers have used ontology editors, which are 4.4.6 OTHER ONTOLOGY OPERATIONS environments or tools used directly for editing, developing or modifying There are many operations that could apply ontologies. They are used to provide to ontology, such as changing [23], which is support to the ontological development considered one of the most interesting and process, as well as to conceptualise the important operations that should be taken ontology; they transform the into account when dealing with ontology. conceptualisation into an executable code In general, most existing ontologies have using translators. Therefore, the output large sizes and complex structures. In fact, ontology of these tools will be in one of the several factors could be responsible for a Web ontology languages supported by change in ontology, including a response to editors such as Protégé [30], OWL-P [8] and new needs or requirements, a change by the OilEd [3]. Alternatively, ontology reasoners developer or the editor of ontology, an are used to check the conflicts with a high ontological translation from one language degree of automation. Many such systems to another and a change of domain of have recently been developed, including interest. On the other hand, using RACER [17] and FaCT [37]. versioning could help to reduce those Returning to the main concern of this problems by keeping track of the section, modelling web languages, there are relationships between different revisions of in general two different types: presentation ontology [23]. As argued in [23] ontology languages such as HTML, designed to versioning is the ability to handle changes represent text and images to users or in ontologies by creating and managing requesters without reference to the content, different ontological variants. and data languages, intended to be processed by machines. The present 5. ONTOLOGY LANGUAGES, ONTOLOGY research relates to the latter. EDITORS AND DEVELOPMENT TOOLS Before OWL, much research had been The main object of semantic web languages conducted into creating a powerful [35] is to add semantics to the existing ontology modelling language. This research information on the Web. RDF/RDFS [5], stream began with the XML-based RDF and OIL [15], DAML+OIL [18] and OWL [1] are RDF/S, progressed to the Ontology modelling web languages that have been Inference Layer (OIL) and continued with developed to represent or express the creation of DAML+OIL, the result of ontologies. In general, most of them are joining the American proposal DAML- based on XML [4] syntax, but they have ONT5 with the European language OIL. All different terminologies and expressions. these languages are based on XML or RDF

191 syntax and are consequently compatible activities. with web standards. Indeed, RDF and OWL make searching for and reusing 8. CONCLUSION information both easier and more reliable, The goal of an ontology is to achieve a because they are considered as standards common and shared knowledge that can be that enable the Web to be a global transmitted between people and between infrastructure for sharing documents and application systems. Thus, ontologies play data equally. an important role in achieving As mentioned in [1, 35], some important interoperability across organizations and requirements for quality support should be on the Semantic Web, because they aim to taken into account when developing capture domain knowledge and their role is languages for encoding ontologies. These to create semantics explicitly in a generic include giving the user explicit written way, providing the basis for agreement format, ease of use, expressive power, within a domain. Thus, ontologies have compatibility, sharing and versioning, become a popular research topic in many internationalisation, formal communities. In fact, ontology is a main conceptualisations of domain models, well- component of this research; therefore, the defined syntax and semantics, efficient definition, structure and the main reasoning support, sufficient expressive operations and applications of ontology are power and convenience of expression. provided. Syntax is one of the most important features of any language, so it should be well-defined; it is also the most significant 9. REFERENCE condition required for the processing of 1. G. Antoniou and F.V. Harmelen, "Web Ontology information by machine. Language: OWL", Presented at Handbook on The semantics of knowledge should be well Ontologies, 2004, pp.67-92. defined, because it represents the meaning 2. T. Berners-Lee, J. Hendler, and O. Lassila, "The Semantic Web", Scientific Am, May 2001, pp. 34– of that knowledge. Formal semantics 43 should be established in the domain of 3. S. Bechhofer, I. Horrocks, C.A. Goble, and R. mathematical logic in a clearly defined way Stevens, "OilEd: a Reason-able Ontology Editor that will lead to unambiguous meaning, for the Semantic Web", In Proceedings of since well defined semantics will lead to Description Logics, 2001. correct reasoning. Semantics can be 4. T. Bray, J. Paoli, and C.M. Sperberg-McQueen, considered a prerequisite to support "Extensible Markup Language (XML)", Presented reasoning. On the other hand, reasoning at Journal, 1997, pp.27-66. 5. D. Brickley and R. Guha, "Resource Description will help to check and discover consistent Framework (RDF) Schema specification", 2000. ontology, to verify unintended http://www.w3.org/TR/RDF-schema. relationships between classes and to 6. H. Chalupsky, "OntoMorph: A Translation classify individuals into classes. System for Symbolic Knowledge", In Proceedings This section has detailed the most common of KR, 2000, pp.471-482. and important languages, RDF, RDF/S, 7. O. Corcho and A. Gómez-Pérez, "Solving DAML+OIL and OWL, all of which are Integration Problems of E-Commerce Standards and Initiatives through Ontological Mappings", In based on XML. XML itself [4] is widely Proceedings of IJCAI 2001 Workshop on E- known in the WWW community, because it Business & the Intelligent Web, Seattle, USA, 2001. is a flexible text format designed to describe 8. N. Desai, A.U. Mallya, A.K. Chopra, and M.P. data and to meet the challenges of large- Singh, "OWL-P: A Methodology for Business scale e-business and electronic publishing, Process Development", In Proceedings of AOIS, which plays an important role in 2005, pp.79-94. exchanging different types of data on the 9. D. Dou, D. McDermott, and P. Qi, "Ontology Web. In fact, it is the basis of a rapidly Translation on the Semantic Web", Presented at on Data Semantics Journal, 3360:35–57, 2005. growing number of software development 10. M. Ehrig, "Ontology Alignment: Bridging the

192

Semantic Gap (Semantic Web and Beyond)", New Patil, T.E. Senator, and W.R. Swartout, "Enabling York, Springer, 2006. Technology for Knowledge Sharing", Presented 11. M. Ehrig and S. Staab, "QOM - Quick Ontology at AI Magazine, 1991, pp.36-56. Mapping", In Proceedings of International 29. N.F. Noy, ": A Survey Of Semantic Web Conference, 2004, pp.683-697. Ontology-Based Approaches", Presented at 12. M. Ehrig and J. Euzenat, "State of the Art on SIGMOD Record, 2004, pp.65-70. Ontology Alignment", Knowledge Web 30. N.F. Noy and M.A. Musen, "PROMPT: Algorithm Deliverable D2.2.3, University of Karlsruhe, 2004. and Tool for Automated Ontology Merging and 13. J. Euzenat and P. Shvaiko, "Ontology Matching", Alignment", In Proceedings of AAAI/IAAI, 2000, Springer-Verlag, Heidelberg (DE), 2007. pp.450-455. 14. J. Euzenat and P. Valtchev, “Similarity-Based 31. M. Paolucci, T. Kawamura, T.R. Payne, and K.P. Ontology Alignment in OWL-Lite", In Sycara, " of Web Services Proceedings of ECAI, 2004, pp.333-337. Capabilities", In Proceedings of International 15. D. Fensel, F.V. Harmelen, I. Horrocks, D.L. Semantic Web Conference, 2002, pp.333-347. McGuinness, and P.F. Patel-Schneider, "OIL: An 32. E. Rahm, P.A. Bernstein, "A Survey of Ontology Infrastructure for the Semantic Web", Approaches to Automatic Schema Matching", Presented at IEEE Intelligent Systems, 2001, pp.38- Presented at VLDB Journal, 2001, pp.334–350. 45. 33. M. Schorlemmer and Y. Kalfoglou, "Progressive 16. D. Fensel, "Ontologies: Silver Bullet for Ontology Alignment for Meaning Coordination: and Electronic An Information-theoretic Foundation", In Commerce", Springer, 2001. Proceedings of the Fourth International Joint 17. V. Haarslev and R. Moller, "RACER System Conference on Autonomous Agents and Description", In Proceedings of IJCAR, 2001, Multiagent Systems, Utrecht, The Netherlands, pp.701-706. 2005, pp. 737–744. 18. F. van Harmelen, P.F. Patel-Schneider, and I. 34. P. Shvaiko and J. Euzenat, "A Survey of Schema- Horrocks (Editors), "Reference Description of the Based Matching Approaches", Presented at DAML+OIL Ontology Markup Language", Journal Data Semantics IV, 2005, pp.146-171. http://www.daml.org/2000/12/reference.html, 35. M.Taye, “ Ontology Alignment Mechanisms for 2000. Improving Web-based Searching”, Ph.D. Thesis, 19. F. Giunchiglia, P. Shvaiko, and M. Yatskevich, De Montfort University, United Kingdom, "Semantic Schema Matching", In Proceedings of England, 2009. OTM Conferences (1), 2005, pp.347-365. 36. D. Tidwell, "Web Services-The Web’s Next 20. C. Ghidini and F. Giunchiglia, "A Semantics for Revolution", IBM Web Service Tutorial, 29 Nov. Abstraction", In Proceedings of ECAI, 2004, 2000, http://www- pp.343-347. 106.ibm.com/developerworks/edu/ws-dw- 21. O. Gotoh, "An Improved Algorithm for Matching wsbasics-i.. Biological Sequences", Presented at Journal of 37. D. Tsarkov and I. Horrocks, "FaCT++ Description Molecular Biology, 162:705-708, 1982. Logic Reasoner: System Description", In 22. Y. Kalfoglou and W.M. Schorlemmer, "IF-Map: Proceedings of the International Joint Conference An Ontology-Mapping Method Based on on (IJCAR), volume 4130, Information-Flow Theory", Presented at Journal pages 292–297, 2006. Data Semantics, 2003, pp.98-127. 38. M. Uschold and M. Gruninger, "Ontologies: 23. M.C.A. Klein and D. Fensel, "Ontology Versioning Principles, Methods and on the Semantic Web", In Proceedings of SWWS, Applications", Knowledge Engineering 2001, pp.75-91. Review., vol. 11, no. 2, June 1996. 24. P. Lambrix and H. Tan, "A Tool for Evaluating Ontology Alignment Strategies", Presented at Journal Data Semantics, 2007, pp.182-202. 25. M. Li, M. Baker, "The Grid: Core Technologies", John Willey & Sons England (2005). 26. G.A. Miller, “WordNet: A Lexical for English", presented at Commun. ACM, 1995, pp.39-41. 27. H. Mihoubi, A. Simonet, and M. Simonet, "An Ontology Driven Approach to Ontology Translation", In Proceedings of DEXA, 2000, pp.573-582 28. R. Neches, R. Fikes, T.W. Finin, T.R. Gruber, R.S.